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\begin{abstract}{剩余寿命预测, 复杂退化过程, 性能影响因素解纠缠, 退化轨迹分解, 双通道架构}
在工业智能化持续推进的背景下，预测性维护（Predictive Maintenance, PdM）正逐步取代传统的定期维护模式，成为保障设备可靠性与生产安全的关键手段。作为PdM体系中的核心支撑技术，故障预测与健康管理（Prognostics and Health Management, PHM）日益受到工业界的广泛关注。剩余寿命（Remaining Useful Life, RUL）预测作为 PHM 的关键组成部分，在面对复杂退化过程时，常因退化行为的高度非线性、非单调性以及外部扰动的显著影响而面临建模难度大、预测精度低等挑战。传统方法在表达能力与建模精度方面存在明显不足，难以有效适应复杂工况下的性能演化模式。

针对上述问题，本文提出了一种基于退化轨迹分解的双通道剩余寿命预测方法（DTDDC）。该方法以设备退化的物理机制为指导，首次将退化过程中的性能演化拆分为具有明确物理语义的“日常损耗项”与“事件扰动项”两类成分，并为其分别设计独立建模通道，最终通过注意力引导的融合策略与回归预测模型完成高精度的剩余寿命估计。

本研究的主要贡献包括：

（1）提出了一种基于退化成因解耦的退化轨迹分解策略。在加法建模假设下，将设备性能退化轨迹结构性拆解为由日常运行损耗和偶发扰动事件共同驱动的两个子轨迹。该策略结合维纳过程建模与趋势提取机制，对稳定演化的退化趋势进行建模，进而通过差分方式提取反映非平稳扰动特征的事件扰动项，实现复杂退化轨迹的可解释性重构。该特征解耦机制有效降低了建模复杂度，并提升了对非平稳退化行为的刻画能力与模型预测的准确性。

（2）设计了一种结构清晰、任务分工明确的双通道剩余寿命预测模型。其中，日常损耗通道基于长短时记忆网络（LSTM）对平稳演化行为进行建模，提升模型的稳定性与泛化能力；事件扰动通道则聚焦于突发退化事件的建模，首先通过事件提取与预处理构建具有语义表达能力的事件表示，随后采用注意力机制增强的多层编码器网络挖掘关键退化模式。在此基础上，本文进一步引入一种注意力引导的特征融合机制，将事件扰动项作为主导信息流，并引入日常损耗特征动态生成注意力权重，引导特征融合过程聚焦于对剩余寿命预测最具贡献的关键因素，显著提升了融合特征的表达能力与鲁棒性。最终通过回归模型输出剩余寿命预测结果，构建完整的预测流程。

为验证所提方法的有效性，本文在一个仿真数据集（C-MAPSS）和一个真实数据集（交流接触器）上与多种主流剩余寿命预测方法进行了对比实验。实验从多个性能指标对模型的准确性、稳定性与泛化能力进行了系统评估，结果表明，所提DTDDC方法在多种退化模式下均展现出显著优于现有方法的预测性能，尤其在复杂退化环境中表现出更强的鲁棒性与适应性。
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\begin{englishabstract}{Remaining Useful Life Prediction, Complex Degradation Process, Performance Factor Decoupling, Degradation Trajectory Decomposition, Dual-Channel Framework}
With the continuous advancement of industrial intelligence, Predictive Maintenance (PdM) is gradually replacing traditional periodic maintenance, becoming a key approach to ensuring equipment reliability and operational safety. As a central technology within the PdM framework, Prognostics and Health Management (PHM) has attracted increasing attention from both academia and industry. Remaining Useful Life (RUL) prediction, a core component of PHM, faces considerable challenges in the context of complex degradation processes due to their highly nonlinear, non-monotonic behavior and susceptibility to external disturbances. Traditional methods often fall short in terms of modeling capacity and prediction accuracy, limiting their effectiveness under complex operational conditions.

To address these challenges, this paper proposes a novel degradation trajectory decomposition-based dual-channel RUL prediction method (DTDDC). Guided by physical degradation mechanisms, the proposed approach decomposes the performance degradation process into two physically meaningful components: the routine wear component and the event disturbance component. Each is modeled via independent learning channels, followed by an attention-guided fusion strategy and regression-based prediction for accurate RUL estimation.

The main contributions of this work are as follows:

(1) A degradation trajectory decomposition strategy is proposed based on causal decoupling of degradation mechanisms. Under an additive modeling assumption, the overall degradation trajectory is structurally decomposed into two sub-trajectories driven by routine operational wear and occasional degradation events. The routine component is modeled via a Wiener process and trend extraction mechanism, while the event disturbance component is derived through a differencing strategy that captures non-stationary perturbations. This decomposition framework enables interpretable reconstruction of complex degradation patterns, effectively reduces modeling difficulty, and enhances prediction accuracy in non-stationary degradation scenarios.

(2) A structurally clear and functionally decoupled dual-channel RUL prediction architecture is designed. The routine wear channel employs a Long Short-Term Memory (LSTM) network to model stable degradation dynamics, ensuring prediction stability and generalization. The event disturbance channel focuses on degradation events, performing event extraction and semantic representation enhancement, followed by a multi-layer encoder network augmented with attention mechanisms to capture key degradation patterns. Furthermore, an attention-guided feature fusion mechanism is introduced, where the event channel serves as the dominant information stream while the routine wear features dynamically generate attention weights to guide fusion towards the most RUL-informative factors. This enhances the expressiveness and robustness of the fused representation. A regression model is then used to produce the final RUL prediction.

To validate the effectiveness of the proposed DTDDC method, extensive experiments are conducted on both a benchmark simulation dataset (C-MAPSS) and a real-world dataset (AC contactors), comparing against several classical and state-of-the-art RUL prediction methods. Experimental results demonstrate that DTDDC achieves consistently superior performance across diverse degradation modes, particularly exhibiting strong robustness and generalizability under complex degradation scenarios.
\end{englishabstract}
